Graphics processor unit acceleration enables realtime endovascular Doppler optical coherence tomography imaging: development and validation (Conference Presentation)
Bibliographic record
Abstract
Endovascular Optical Coherence Tomography (OCT) has previously been used in both bench-top and clinical environments to produce vascular images, and can be helpful in characterizing, among other pathologies, plaque build-up and impedances to normal blood flow. The raw data produced can also be processed to yield high-resolution blood velocity information, but this computation is expensive and has previously only been available a posteriori using post-processing software. Real-time Doppler OCT (DOCT) imaging has been demonstrated before in the skin and eye, but this capability has not been available to vascular surgeons. Graphics Processing Units (GPUs) can be used to dramatically accelerate this type of distributed computation. In this paper we present a software package capable of real-time DOCT processing and circular image display using GPU acceleration designed to operate with catheter-based clinical OCT systems. This image data is overlayed onto structural images providing clinicians with live, high-resolution blood velocity information to complement anatomical data. Further, we validated flow data obtained in real time using a carotid flow phantom -- constructed using 3D structural OCT data -- and controlled flow from an external pump.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".